Hae Sol Moon Defends PhD on Alzheimer’s Modeling

Congratulations! Dr. Hae Sol Moon has successfully defended his PhD on “Integrative Modeling of Alzheimer’s Disease Risk”.

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Celebrating Dr. Alex Allphin’s PhD Defense!

Huge congratulations to Dr. Alex Allphin on a successful PhD defense! A key member of our QIAL lab, Alex’s work has pushed the boundaries of imaging science.

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Mapping Exercise-Induced Brain Changes in Alzheimer’s Disease

Voluntary & enforced exercise drive distinct brain changes in Alzheimer’s models.
MRI reveals how emotion, memory, & motor circuits adapt—showing exercise’s neuroprotective power.

Badea A, Mahzarnia A, Reddy D, et al. Neuroimaging biomarkers of neuroprotection: Impact of voluntary vs. enforced exercise in Alzheimer’s disease models. Magn Reson Imaging. 2025;110406. doi: 10.1016/j.mri.2025.110406

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APOE-Dependent Cardiac Response to Exercise in Mice via PCCT

This study investigates the impact of exercise on cardiac function across different APOE genotypes in mice using low-dose cardiac photon-counting micro-CT and deep learning-based segmentation. Results show significant, genotype-dependent improvements in cardiac performance, with the largest effects observed in APOE3 and APOE4 mice.

Allphin AJ, Nadkarni R, Han ZY, Clark DP, Ghaghada KB, Badea A, Badea CT. Assessing the cardioprotective effects of exercise in APOE mouse models using deep learning and photon-counting micro-CT. PLOS ONE. 2025 Apr 10; https://doi.org/10.1371/journal.pone.0320892.

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QIAL Contributions to SPIE Medical Imaging 2025

Advancing Preclinical and AI-Driven Imaging  with our Work presented at SPIE Medical Imaging 2025:

  • Deve​lopment of a virtual photon-counting micro-CT imaging platform for preclinical cancer studies
    Nadkarni R, Clark D, Allphin A, Qi Y, Mowery Y, Segars W, Badea CT.
    Presented on April 8, 2025.
    https://doi.org/10.1117/12.3047047

  • A deep-learning reconstruction framework for low-dose, dynamic x-ray CT
    Clark D, Badea CT.
    Presented on April 8, 2025.
    https://doi.org/10.1117/12.3047120

  • Deep-learning micro-CT perfusion quantification
    Allphin A, Nadkarni R, Clark D, Badea CT.
    Presented on April 8, 2025.
    https://doi.org/10.1117/12.3047483

  • A perfusion phantom for dynamic micro-CT imaging
    Allphin A, Badea A, Clark D, Badea CT.
    Presented on April 8, 2025.
    https://doi.org/10.1117/12.3047282

  • High-resolution micro-CT imaging protocols for studies of intracranial aneurysms in murine models
    Badea CT, Chaparro E, Mace B, Allphin A, Nadkarni R, Han Z, Qi Y, Clark D, Hasan D.
    Presented on April 2, 2025.
    https://doi.org/10.1117/12.3046744

  • Quantitative comparison of femurs in apolipoprotein E mouse models using high-resolution photon-counting micro-CT
    Nadkarni R, Han Z, Allphin A, Clark D, Badea A, Badea CT.
    Presented on April 2, 2025.
    https://doi.org/10.1117/12.3047137

  • Exploring VivoVist™ for ex vivo micro-CT imaging of mouse vasculature
    Badea CT, Nadkarni R, Allphin A, Qi Y, Clark D, Hainfeld J.
    Presented on April 2, 2025.
    https://doi.org/10.1117/12.3046768

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Multiscale mapping of brain connections in Alzheimer’s risk

We developed a multiscale method to map how aging and APOE risk factors impact brain connections in Alzheimer’s. Frontal & occipital bundles emerge as key predictors. Early detection might start with olfactory memory.

Stout, J., Anderson, R. J., Mahzarnia, A., Han, Z. Y., Beck, K., Browndyke, J., Johnson, K., O’Brien, R. J., & Badea, A. (2025). Mapping the impact of age and APOE risk factors for late onset Alzheimer’s disease on long range brain connections through multiscale bundle analysis. Brain Structure and Function, 230, 45. https://doi.org/10.1007/s00429-025-02905-9

 

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Nanoparticle Contrast Agents for Photon-Counting Computed Tomography

Our  review article is published:
Devkota L, Bhavane R, Badea CT, Tanifum EA, Annapragada AV, Ghaghada KB. Nanoparticle Contrast Agents for Photon-Counting Computed Tomography: Recent Developments and Future Opportunities. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2025 Jan-Feb;17(1):e70004. doi: 10.1002/wnan.70004. PMID: 39948059. https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wnan.70004

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Alzheimer’s Risk in Brain Networks

Our  study links APOE gene, age, sex, and diet to brain network changes in Alzheimer’s disease.

Winter, S., Mahzarnia, A., Anderson, R. J., Han, Z. Y., Tremblay, J., Stout, J. A., Moon, H. S., Marcellino, D., Dunson, D. B., & Badea, A. (2024). Brain network fingerprints of Alzheimer’s disease risk factors in mouse models with humanized APOE alleles. Magnetic Resonance Imaging, 114, 110251. https://doi.org/10.1016/j.mri.2024.110251

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Fast Denoising of 5D Cardiac Photon-Counting CT

Using deep learning denoising we achieve a 32x speedup over iterative methods for 5D cardiac PCCT in the mouse!

Rohan Nadkarni, Darin P. Clark, Alex Allphin, and Cristian T. Badea. Investigating Deep Learning Strategies for Fast Denoising of 5D Cardiac Photon-Counting Micro-CT Images.2024. Phys. Med. Biol. https://doi.org/10.1088/1361-6560/ad7fc6

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New Turn-Table Micro-CT Scanner for Perfusion Imaging

Our new turn-table micro-CT scanner offers high-resolution, low-dose dynamic perfusion imaging in mice.

Allphin AJ, Nadkarni R, Clark DP, Gil CJ, Tomov M, Serpooshan V, Badea CT. Turn-table micro-CT scanner for dynamic perfusion imaging in mice: design, implementation, and evaluation. Phys Med Biol. 2024 Aug 13. doi: 10.1088/1361-6560/ad6edd.  PMID: 39137802.

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